Overview

Dataset statistics

Number of variables26
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.8 KiB
Average record size in memory208.6 B

Variable types

NUM16
CAT10

Reproduction

Analysis started2020-07-29 07:22:51.235767
Analysis finished2020-07-29 07:23:51.106828
Duration59.87 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

CarName has a high cardinality: 147 distinct values High cardinality
highwaympg is highly correlated with citympgHigh correlation
citympg is highly correlated with highwaympgHigh correlation
fuelsystem is highly correlated with fueltypeHigh correlation
fueltype is highly correlated with fuelsystemHigh correlation
CarName is uniformly distributed Uniform
car_ID has unique values Unique
symboling has 67 (32.7%) zeros Zeros

Variables

car_ID
Real number (ℝ≥0)

UNIQUE

Distinct count205
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.0
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:51.231837image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.2
Q152
median103
Q3154
95-th percentile194.8
Maximum205
Range204
Interquartile range (IQR)102

Descriptive statistics

Standard deviation59.32256457
Coefficient of variation (CV)0.5759472288
Kurtosis-1.2
Mean103
Median Absolute Deviation (MAD)51
Skewness0
Sum21115
Variance3519.166667
2020-07-29T15:23:51.427498image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20510.5%
 
6410.5%
 
7410.5%
 
7310.5%
 
7210.5%
 
7110.5%
 
7010.5%
 
6910.5%
 
6810.5%
 
6710.5%
 
Other values (195)19595.1%
 
ValueCountFrequency (%) 
110.5%
 
210.5%
 
310.5%
 
410.5%
 
510.5%
 
ValueCountFrequency (%) 
20510.5%
 
20410.5%
 
20310.5%
 
20210.5%
 
20110.5%
 

symboling
Real number (ℝ)

ZEROS

Distinct count6
Unique (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8341463414634146
Minimum-2
Maximum3
Zeros67
Zeros (%)32.7%
Memory size1.6 KiB
2020-07-29T15:23:51.585060image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.245306828
Coefficient of variation (CV)1.492911695
Kurtosis-0.6762713562
Mean0.8341463415
Median Absolute Deviation (MAD)1
Skewness0.2110722721
Sum171
Variance1.550789096
2020-07-29T15:23:51.749725image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
06732.7%
 
15426.3%
 
23215.6%
 
32713.2%
 
-12210.7%
 
-231.5%
 
ValueCountFrequency (%) 
-231.5%
 
-12210.7%
 
06732.7%
 
15426.3%
 
23215.6%
 
ValueCountFrequency (%) 
32713.2%
 
23215.6%
 
15426.3%
 
06732.7%
 
-12210.7%
 

CarName
Categorical

HIGH CARDINALITY
UNIFORM

Distinct count147
Unique (%)71.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
toyota corolla
 
6
peugeot 504
 
6
toyota corona
 
6
subaru dl
 
4
mitsubishi outlander
 
3
Other values (142)
180
ValueCountFrequency (%) 
toyota corolla62.9%
 
peugeot 50462.9%
 
toyota corona62.9%
 
subaru dl42.0%
 
mitsubishi outlander31.5%
 
mitsubishi mirage g431.5%
 
mazda 62631.5%
 
mitsubishi g431.5%
 
honda civic31.5%
 
toyota mark ii31.5%
 
Other values (137)16580.5%
 
2020-07-29T15:23:51.946565image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length31
Median length13
Mean length14.14634146
Min length6

fueltype
Categorical

HIGH CORRELATION

Distinct count2
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
gas
185
diesel
 
20
ValueCountFrequency (%) 
gas18590.2%
 
diesel209.8%
 
2020-07-29T15:23:52.135300image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.292682927
Min length3

aspiration
Categorical

Distinct count2
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
std
168
turbo
37
ValueCountFrequency (%) 
std16882.0%
 
turbo3718.0%
 
2020-07-29T15:23:52.338410image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.36097561
Min length3

doornumber
Categorical

Distinct count2
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
four
115
two
90
ValueCountFrequency (%) 
four11556.1%
 
two9043.9%
 
2020-07-29T15:23:52.525836image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.56097561
Min length3

carbody
Categorical

Distinct count5
Unique (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
sedan
96
hatchback
70
wagon
25
hardtop
 
8
convertible
 
6
ValueCountFrequency (%) 
sedan9646.8%
 
hatchback7034.1%
 
wagon2512.2%
 
hardtop83.9%
 
convertible62.9%
 
2020-07-29T15:23:52.722644image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length11
Median length5
Mean length6.619512195
Min length5

drivewheel
Categorical

Distinct count3
Unique (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
fwd
120
rwd
76
4wd
 
9
ValueCountFrequency (%) 
fwd12058.5%
 
rwd7637.1%
 
4wd94.4%
 
2020-07-29T15:23:52.911519image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

enginelocation
Categorical

Distinct count2
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
front
202
rear
 
3
ValueCountFrequency (%) 
front20298.5%
 
rear31.5%
 
2020-07-29T15:23:53.108423image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.985365854
Min length4

wheelbase
Real number (ℝ≥0)

Distinct count53
Unique (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.75658536585367
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:53.281547image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93.02
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.021775685
Coefficient of variation (CV)0.06097594062
Kurtosis1.017038946
Mean98.75658537
Median Absolute Deviation (MAD)2.7
Skewness1.050213776
Sum20245.1
Variance36.2617824
2020-07-29T15:23:53.446293image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
94.52110.2%
 
93.7209.8%
 
95.7136.3%
 
96.583.9%
 
98.473.4%
 
97.373.4%
 
96.362.9%
 
107.962.9%
 
98.862.9%
 
99.162.9%
 
Other values (43)10551.2%
 
ValueCountFrequency (%) 
86.621.0%
 
88.410.5%
 
88.621.0%
 
89.531.5%
 
91.321.0%
 
ValueCountFrequency (%) 
120.910.5%
 
115.621.0%
 
114.242.0%
 
11321.0%
 
11210.5%
 

carlength
Real number (ℝ≥0)

Distinct count75
Unique (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.04926829268288
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:53.603895image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.14
Q1166.3
median173.2
Q3183.1
95-th percentile196.36
Maximum208.1
Range67
Interquartile range (IQR)16.8

Descriptive statistics

Standard deviation12.33728853
Coefficient of variation (CV)0.0708838862
Kurtosis-0.08289485345
Mean174.0492683
Median Absolute Deviation (MAD)6.9
Skewness0.1559537713
Sum35680.1
Variance152.2086882
2020-07-29T15:23:53.783676image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
157.3157.3%
 
188.8115.4%
 
166.373.4%
 
171.773.4%
 
186.773.4%
 
165.362.9%
 
177.862.9%
 
176.262.9%
 
186.662.9%
 
176.852.4%
 
Other values (65)12962.9%
 
ValueCountFrequency (%) 
141.110.5%
 
144.621.0%
 
15031.5%
 
155.931.5%
 
156.910.5%
 
ValueCountFrequency (%) 
208.110.5%
 
202.621.0%
 
199.621.0%
 
199.210.5%
 
198.942.0%
 

carwidth
Real number (ℝ≥0)

Distinct count44
Unique (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.90780487804878
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:53.941211image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.9
95-th percentile70.46
Maximum72.3
Range12
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.145203853
Coefficient of variation (CV)0.03254855562
Kurtosis0.7027642441
Mean65.90780488
Median Absolute Deviation (MAD)1.4
Skewness0.9040034988
Sum13511.1
Variance4.60189957
2020-07-29T15:23:54.097452image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
63.82411.7%
 
66.52311.2%
 
65.4157.3%
 
63.6115.4%
 
64.4104.9%
 
68.4104.9%
 
6494.4%
 
65.583.9%
 
65.273.4%
 
66.362.9%
 
Other values (34)8240.0%
 
ValueCountFrequency (%) 
60.310.5%
 
61.810.5%
 
62.510.5%
 
63.410.5%
 
63.6115.4%
 
ValueCountFrequency (%) 
72.310.5%
 
7210.5%
 
71.731.5%
 
71.431.5%
 
70.910.5%
 

carheight
Real number (ℝ≥0)

Distinct count49
Unique (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.72487804878049
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:54.253672image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.44352197
Coefficient of variation (CV)0.04548213153
Kurtosis-0.4438123651
Mean53.72487805
Median Absolute Deviation (MAD)1.6
Skewness0.06312273247
Sum11013.6
Variance5.970799617
2020-07-29T15:23:54.409878image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
50.8146.8%
 
52125.9%
 
55.7125.9%
 
54.5104.9%
 
54.1104.9%
 
55.594.4%
 
56.783.9%
 
54.383.9%
 
51.673.4%
 
56.173.4%
 
Other values (39)10852.7%
 
ValueCountFrequency (%) 
47.810.5%
 
48.821.0%
 
49.421.0%
 
49.642.0%
 
49.731.5%
 
ValueCountFrequency (%) 
59.821.0%
 
59.131.5%
 
58.742.0%
 
58.310.5%
 
57.531.5%
 

curbweight
Real number (ℝ≥0)

Distinct count171
Unique (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.5658536585365
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:54.762530image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1901
Q12145
median2414
Q32935
95-th percentile3503
Maximum4066
Range2578
Interquartile range (IQR)790

Descriptive statistics

Standard deviation520.6802035
Coefficient of variation (CV)0.2037436064
Kurtosis-0.0428537661
Mean2555.565854
Median Absolute Deviation (MAD)386
Skewness0.6813981891
Sum523891
Variance271107.8743
2020-07-29T15:23:54.920152image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
238542.0%
 
198931.5%
 
191831.5%
 
227531.5%
 
323021.0%
 
241021.0%
 
325221.0%
 
233721.0%
 
240321.0%
 
241421.0%
 
Other values (161)18087.8%
 
ValueCountFrequency (%) 
148810.5%
 
171310.5%
 
181910.5%
 
183710.5%
 
187421.0%
 
ValueCountFrequency (%) 
406621.0%
 
395010.5%
 
390010.5%
 
377010.5%
 
375010.5%
 

enginetype
Categorical

Distinct count7
Unique (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
ohc
148
ohcf
 
15
ohcv
 
13
dohc
 
12
l
 
12
Other values (2)
 
5
ValueCountFrequency (%) 
ohc14872.2%
 
ohcf157.3%
 
ohcv136.3%
 
dohc125.9%
 
l125.9%
 
rotor42.0%
 
dohcv10.5%
 
2020-07-29T15:23:55.091990image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.126829268
Min length1

cylindernumber
Categorical

Distinct count7
Unique (%)3.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
four
159
six
 
24
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2
ValueCountFrequency (%) 
four15977.6%
 
six2411.7%
 
five115.4%
 
eight52.4%
 
two42.0%
 
twelve10.5%
 
three10.5%
 
2020-07-29T15:23:55.295096image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.902439024
Min length3

enginesize
Real number (ℝ≥0)

Distinct count44
Unique (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.90731707317073
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:55.451282image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median120
Q3141
95-th percentile201.2
Maximum326
Range265
Interquartile range (IQR)44

Descriptive statistics

Standard deviation41.64269344
Coefficient of variation (CV)0.3281346923
Kurtosis5.305682092
Mean126.9073171
Median Absolute Deviation (MAD)23
Skewness1.947655045
Sum26016
Variance1734.113917
2020-07-29T15:23:55.607493image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
122157.3%
 
92157.3%
 
98146.8%
 
97146.8%
 
108136.3%
 
90125.9%
 
110125.9%
 
10983.9%
 
12073.4%
 
14173.4%
 
Other values (34)8842.9%
 
ValueCountFrequency (%) 
6110.5%
 
7031.5%
 
7910.5%
 
8010.5%
 
90125.9%
 
ValueCountFrequency (%) 
32610.5%
 
30810.5%
 
30410.5%
 
25821.0%
 
23421.0%
 

fuelsystem
Categorical

HIGH CORRELATION

Distinct count8
Unique (%)3.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
mpfi
94
2bbl
66
idi
20
1bbl
 
11
spdi
 
9
Other values (3)
 
5
ValueCountFrequency (%) 
mpfi9445.9%
 
2bbl6632.2%
 
idi209.8%
 
1bbl115.4%
 
spdi94.4%
 
4bbl31.5%
 
mfi10.5%
 
spfi10.5%
 
2020-07-29T15:23:55.788001image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.897560976
Min length3

boreratio
Real number (ℝ≥0)

Distinct count38
Unique (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.329756097560975
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:55.961123image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.2708437054
Coefficient of variation (CV)0.08134040377
Kurtosis-0.7850418332
Mean3.329756098
Median Absolute Deviation (MAD)0.26
Skewness0.0201564181
Sum682.6
Variance0.07335631277
2020-07-29T15:23:56.126227image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.622311.2%
 
3.19209.8%
 
3.15157.3%
 
2.97125.9%
 
3.03125.9%
 
3.4694.4%
 
3.7883.9%
 
3.4383.9%
 
3.3183.9%
 
3.2773.4%
 
Other values (28)8340.5%
 
ValueCountFrequency (%) 
2.5410.5%
 
2.6810.5%
 
2.9173.4%
 
2.9210.5%
 
2.97125.9%
 
ValueCountFrequency (%) 
3.9421.0%
 
3.821.0%
 
3.7883.9%
 
3.7610.5%
 
3.7431.5%
 

stroke
Real number (ℝ≥0)

Distinct count37
Unique (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.255414634146341
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:56.308007image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.41
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.3135970138
Coefficient of variation (CV)0.0963308976
Kurtosis2.174396435
Mean3.255414634
Median Absolute Deviation (MAD)0.14
Skewness-0.6897045784
Sum667.36
Variance0.09834308704
2020-07-29T15:23:56.448598image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.4209.8%
 
3.03146.8%
 
3.15146.8%
 
3.23146.8%
 
3.39136.3%
 
2.64115.4%
 
3.3594.4%
 
3.2994.4%
 
3.4683.9%
 
3.4162.9%
 
Other values (27)8742.4%
 
ValueCountFrequency (%) 
2.0710.5%
 
2.1921.0%
 
2.3610.5%
 
2.64115.4%
 
2.6821.0%
 
ValueCountFrequency (%) 
4.1721.0%
 
3.931.5%
 
3.8642.0%
 
3.6452.4%
 
3.5862.9%
 

compressionratio
Real number (ℝ≥0)

Distinct count32
Unique (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.142536585365855
Minimum7.0
Maximum23.0
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:56.621727image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.82
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.972040322
Coefficient of variation (CV)0.3916219861
Kurtosis5.233054348
Mean10.14253659
Median Absolute Deviation (MAD)0.4
Skewness2.610862458
Sum2079.22
Variance15.77710432
2020-07-29T15:23:56.791600image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
94622.4%
 
9.42612.7%
 
8.5146.8%
 
9.5136.3%
 
9.3115.4%
 
8.794.4%
 
9.283.9%
 
883.9%
 
773.4%
 
2152.4%
 
Other values (22)5828.3%
 
ValueCountFrequency (%) 
773.4%
 
7.552.4%
 
7.642.0%
 
7.721.0%
 
7.810.5%
 
ValueCountFrequency (%) 
2352.4%
 
22.710.5%
 
22.531.5%
 
2210.5%
 
21.910.5%
 

horsepower
Real number (ℝ≥0)

Distinct count59
Unique (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.1170731707317
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:56.949213image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile180.8
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.54416681
Coefficient of variation (CV)0.3798048255
Kurtosis2.68400616
Mean104.1170732
Median Absolute Deviation (MAD)25
Skewness1.405310154
Sum21344
Variance1563.741129
2020-07-29T15:23:57.105429image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
68199.3%
 
70115.4%
 
69104.9%
 
11694.4%
 
11083.9%
 
9573.4%
 
8862.9%
 
6262.9%
 
16062.9%
 
10162.9%
 
Other values (49)11757.1%
 
ValueCountFrequency (%) 
4810.5%
 
5221.0%
 
5510.5%
 
5621.0%
 
5810.5%
 
ValueCountFrequency (%) 
28810.5%
 
26210.5%
 
20731.5%
 
20010.5%
 
18421.0%
 

peakrpm
Real number (ℝ≥0)

Distinct count23
Unique (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5125.121951219512
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:57.261672image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile5980
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation476.9856431
Coefficient of variation (CV)0.09306815479
Kurtosis0.08675585561
Mean5125.121951
Median Absolute Deviation (MAD)300
Skewness0.07515872237
Sum1050650
Variance227515.3037
2020-07-29T15:23:57.410578image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
55003718.0%
 
48003617.6%
 
50002713.2%
 
52002311.2%
 
5400136.3%
 
600094.4%
 
525073.4%
 
580073.4%
 
450073.4%
 
415052.4%
 
Other values (13)3416.6%
 
ValueCountFrequency (%) 
415052.4%
 
420052.4%
 
425031.5%
 
435042.0%
 
440031.5%
 
ValueCountFrequency (%) 
660021.0%
 
600094.4%
 
590031.5%
 
580073.4%
 
575010.5%
 

citympg
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count29
Unique (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.21951219512195
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:57.551167image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.542141653
Coefficient of variation (CV)0.2594079379
Kurtosis0.5786483405
Mean25.2195122
Median Absolute Deviation (MAD)5
Skewness0.6637040288
Sum5170
Variance42.79961741
2020-07-29T15:23:57.724373image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
312813.7%
 
192713.2%
 
242210.7%
 
27146.8%
 
17136.3%
 
26125.9%
 
23125.9%
 
2183.9%
 
3083.9%
 
2583.9%
 
Other values (19)5325.9%
 
ValueCountFrequency (%) 
1310.5%
 
1421.0%
 
1531.5%
 
1662.9%
 
17136.3%
 
ValueCountFrequency (%) 
4910.5%
 
4710.5%
 
4510.5%
 
3873.4%
 
3762.9%
 

highwaympg
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count30
Unique (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.75121951219512
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:57.889818image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42.8
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.886443131
Coefficient of variation (CV)0.2239404889
Kurtosis0.4400703815
Mean30.75121951
Median Absolute Deviation (MAD)5
Skewness0.5399971879
Sum6304
Variance47.423099
2020-07-29T15:23:58.056816image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
25199.3%
 
24178.3%
 
38178.3%
 
30167.8%
 
32167.8%
 
34146.8%
 
37136.3%
 
28136.3%
 
29104.9%
 
3394.4%
 
Other values (20)6129.8%
 
ValueCountFrequency (%) 
1621.0%
 
1710.5%
 
1821.0%
 
1921.0%
 
2021.0%
 
ValueCountFrequency (%) 
5410.5%
 
5310.5%
 
5010.5%
 
4721.0%
 
4621.0%
 

price
Real number (ℝ≥0)

Distinct count189
Unique (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13276.710570731706
Minimum5118.0
Maximum45400.0
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-07-29T15:23:58.228680image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6197
Q17788
median10295
Q316503
95-th percentile32472.4
Maximum45400
Range40282
Interquartile range (IQR)8715

Descriptive statistics

Standard deviation7988.852332
Coefficient of variation (CV)0.6017192504
Kurtosis3.051647871
Mean13276.71057
Median Absolute Deviation (MAD)3306
Skewness1.777678156
Sum2721725.667
Variance63821761.58
2020-07-29T15:23:58.390083image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
557221.0%
 
669221.0%
 
795721.0%
 
789821.0%
 
622921.0%
 
760921.0%
 
729521.0%
 
8916.521.0%
 
849521.0%
 
884521.0%
 
Other values (179)18590.2%
 
ValueCountFrequency (%) 
511810.5%
 
515110.5%
 
519510.5%
 
534810.5%
 
538910.5%
 
ValueCountFrequency (%) 
4540010.5%
 
4131510.5%
 
4096010.5%
 
3702810.5%
 
3688010.5%
 

Interactions

2020-07-29T15:23:02.392828image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:02.595901image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:02.798977image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:03.010945image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:03.215412image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:03.418758image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:03.621870image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:03.793669image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:04.005893image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:04.211363image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:04.398824image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:04.603268image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:04.790725image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:04.971173image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:05.159888image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:05.362963image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:05.550418image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:05.730222image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:05.919040image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:06.099385image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:06.296441image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:06.485184image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:06.875715image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:07.039922image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:07.196142image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:07.367997image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:07.524209image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:07.697360image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:07.853574image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:08.018490image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:08.176172image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:08.340637image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:08.513787image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:08.685628image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:08.857476image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:09.053788image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:09.227176image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:09.399004image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:09.586431image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:09.781861image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:09.955042image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:10.151553image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:10.324814image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:10.527908image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:10.715376image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:10.887180image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:11.099555image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:11.302628image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:11.507114image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:11.694571image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:11.885229image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:12.333322image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:12.498413image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:12.671703image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:12.837351image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:13.009182image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:13.174305image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:13.347482image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:13.519315image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:13.691176image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:13.871015image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:14.032507image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:14.213141image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:14.386358image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:14.558189image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:14.730037image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:14.901855image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:15.066141image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:15.237945image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:15.411108image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:15.567353image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:15.739155image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:15.895366image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:16.067202image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:16.232263image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:16.404380image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:16.569823image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:16.758633image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:16.946325image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:17.126172image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:17.580778image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:17.752630image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:17.917325image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:18.090442image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:18.270977image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:18.444137image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:18.615941image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:18.787764image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:18.937838image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:19.125267image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:19.290019image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:19.463230image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:19.619415image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:19.791275image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:19.963081image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:20.134928image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:20.315856image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:20.473403image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:20.637900image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:20.810989image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:20.967207image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:21.139040image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:21.304284image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:21.477499image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:21.633712image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:21.805810image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:21.970845image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:22.145510image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:22.351995image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:22.523357image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:22.946384image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:23.102596image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:23.283309image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:23.455145image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:23.616367image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:23.772588image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:23.938881image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:24.084037image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:24.265009image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:24.431003image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:24.588678image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:24.752823image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:24.910378image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:25.066596image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:25.222775image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:25.388512image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:25.546060image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:25.717894image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:25.889737image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-29T15:23:35.060819image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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Correlations

2020-07-29T15:23:58.605031image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-07-29T15:23:58.942901image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-07-29T15:23:59.490983image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-07-29T15:23:59.843217image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-07-29T15:24:00.202525image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-07-29T15:23:49.860852image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-29T15:23:50.817895image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

car_IDsymbolingCarNamefueltypeaspirationdoornumbercarbodydrivewheelenginelocationwheelbasecarlengthcarwidthcarheightcurbweightenginetypecylindernumberenginesizefuelsystemboreratiostrokecompressionratiohorsepowerpeakrpmcitympghighwaympgprice
013alfa-romero giuliagasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212713495.000
123alfa-romero stelviogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212716500.000
231alfa-romero Quadrifogliogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.01545000192616500.000
342audi 100 lsgasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.01025500243013950.000
452audi 100lsgasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.01155500182217450.000
562audi foxgasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.51105500192515250.000
671audi 100lsgasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.51105500192517710.000
781audi 5000gasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.51105500192518920.000
891audi 4000gasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.31405500172023875.000
9100audi 5000s (diesel)gasturbotwohatchback4wdfront99.5178.267.952.03053ohcfive131mpfi3.133.407.01605500162217859.167

Last rows

car_IDsymbolingCarNamefueltypeaspirationdoornumbercarbodydrivewheelenginelocationwheelbasecarlengthcarwidthcarheightcurbweightenginetypecylindernumberenginesizefuelsystemboreratiostrokecompressionratiohorsepowerpeakrpmcitympghighwaympgprice
195196-1volvo 144eagasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.51145400232813415.0
196197-2volvo 244dlgasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.51145400242815985.0
197198-1volvo 245gasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.51145400242816515.0
198199-2volvo 264glgasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.51625100172218420.0
199200-1volvo dieselgasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.51625100172218950.0
200201-1volvo 145e (sw)gasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.51145400232816845.0
201202-1volvo 144eagasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.71605300192519045.0
202203-1volvo 244dlgasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.81345500182321485.0
203204-1volvo 246dieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.01064800262722470.0
204205-1volvo 264glgasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.51145400192522625.0